Abstract
BACKGROUND: Senescence is a complex developmental process that is regulated by a multitude of environmental, genetic, and physiological factors. Optimizing the timing and dynamics of this process has the potential to significantly impact crop adaptation to future climates and for maintaining grain yield and quality, particularly under terminal stress. Accurately capturing the dynamics of senescence and isolating the genetic variance component requires frequent assessment as well as intense field testing. Here, we evaluated and compared the potential of temporally dense drone-based RGB- and multispectral image sequences for this purpose. Regular measurements were made throughout the grain filling phase for more than 600 winter wheat genotypes across three experiments in a high-yielding environment of temperate Europe. At the plot level, multispectral and RGB indices were extracted, and time series were modelled using different parametric and semi-parametric models. The capability of these approaches to track senescence was evaluated based on estimated model parameters, with corresponding parameters derived from repeated visual scorings as a reference. This approach represents the need for remote-sensing based proxies that capture the entire process, from the onset to the conclusion of senescence, as well as the rate of the progression. RESULTS: Our results indicated the efficacy of both RGB and multispectral reflectance indices in monitoring senescence dynamics and accurately identifying key temporal parameters characterizing this phase, comparable to more sophisticated proximal sensing techniques that offer limited throughput. Correlation coefficients of up to 0.8 were observed between multispectral (NDVIred668-index) and visual scoring, respectively 0.9 between RGB (ExGR-index) and visual scoring. Sub-sampling of measurement events demonstrated that the timing and frequency of measurements were highly influential, arguably even more than the choice of sensor. CONCLUSIONS: Remote-sensing based proxies derived from both RGB and multispectral sensors can capture the senescence process accurately. The sub-sampling emphasized the importance of timely and frequent assessments, but also highlighted the need for robust methods that enable such frequent assessments to be made under variable environmental conditions. The proposed measurement and data processing strategies can improve the measurement and understanding of senescence dynamics, facilitating adaptive crop breeding strategies in the context of climate change.